# Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids

arXiv: https://arxiv.org/abs/2306.13397 · HTML: https://mlsanity.com/paper/2306.13397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding](https://mlsanity.com/paper/1712.09005/) (2017)
- [A Survey on Forced Oscillations in Power System](https://mlsanity.com/paper/1612.04718/) (2016)
- [A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters](https://mlsanity.com/paper/1807.02449/) (2018)
- [Time Series Classification for Locating Forced Oscillation Sources](https://mlsanity.com/paper/2004.07072/) (2020)

## Abstract (arXiv)

Time series motifs are used for discovering higher-order structures of time series data. Based on time series motifs, the motif embedding correlation field (MECF) is proposed to characterize higher-order temporal structures of dynamical system time series. A MECF-based unsupervised learning approach is applied in locating the source of the forced oscillation (FO), a periodic disturbance that detrimentally impacts power grids. Locating the FO source is imperative for system stability. Compared with the Fourier analysis, the MECF-based unsupervised learning is applicable under various FO situations, including the single FO, FO with resonance, and multiple sources FOs. The MECF-based unsupervised learning is a data-driven approach without any prior knowledge requirement of system models or typologies. Tests on the UK high-voltage transmission grid illustrate the effectiveness of MECF-based unsupervised learning. In addition, the impacts of coupling strength and measurement noise on locating the FO source by the MECF-based unsupervised learning are investigated.